In some known approaches to speech recognition, words are represented by phone-based Markov models and input speech which, after conversion to a coded sequence of acoustic elements or labels, is decoded by matching the label sequences to these models, using probabilistic algorithms such as Viterbi decoding.
U.S. Pat. No. 4,099,257 to R. F. Arnold et al entitled, "Markov Processor for Context Encoding from Given Characters and for Character Decoding from Given Contexts", and U.S. Pat. No. 4,348,553 to J. K. Baker et al entitled "Parallel Pattern Verifier with Dynamic Time Warping" are representative of systems in which alignment of received information that is to be decoded or identified is performed with respect to Markov models. Recognition errors in speech recognition systems typically occur between similarly sounding words such as "concern" and "concerned". This is due to the fact that major portions of similar words are identically pronounced and thus do not contribute at all to distinction. Only a minor portion in such similar words is different and thus critical for discrimination. Because in known speech recognition systems using statistical word models all phones in the models are treated equal, the really discriminating portions do not contribute properly to the recognition decision.